arXiv subjects

Ben Xu

Publications and source records attributed to Ben Xu.

At least 19 recordsLinked to original sources

arXiv2026

Extreme-Scale Atomistic Simulation of Real-Temperature Magnetic Skyrmion Dynamics by Coupled Spin-Lattice Modeling

Real-temperature topological magnetic dynamics in functional materials is governed by coupled lattice and spin evolution, yet remains inaccessible to predictive simulation at device-relevant scales. As a flagship example, thermally driven helix-to-skyrmion transformation in FeGe requires atomistic resolution, explicit lattice motion, and micrometer-scale domains to resolve device-scale topological texture formation. We combine a spin-constrained density-functional-theory-trained neuro-evolution potential with a structure-preserving spin-lattice integrator within one machine-learned framework. Architecture-specific optimizations, kernel fusion, SVE2 vectorization, and NUMA-aware data layout deliver a seven orders-of-magnitude speedup over prior spin-aware methods. Deployed on LineShine exascale supercomputer, the full application scales to 12.45 million CPU cores with 89.7% weak-scaling efficiency, enabling simulations of 1.34 trillion atoms and an equal number of spins while reaching 48.5 PFLOPS in double precision. The simulations directly resolve real-temperature skyrmion nucleation and reorganization at previously inaccessible scales, establishing a new regime for predictive simulation of coupled spin-lattice topological magnetic dynamics.

cs.DC↗

arXiv2026

Laser-Liquid Interaction in Laser-Induced Forward Transfer (LIFT) Printing: A Multiscale Perspective on Bubble Dynamics and Material Ejection

Laser-induced forward transfer (LIFT) is a nozzle-free laser-assisted printing method that provides an advanced manufacturing route for spatially selective deposition of functional inks, nanoparticle suspensions, polymers, hydrogels, biological materials, and other difficult-to-nozzle formulations. The apparent simplicity of LIFT, however, conceals a strongly coupled laser-liquid interaction. Laser energy is absorbed within a confined donor architecture, converted into thermal and plasma responses, and then transformed into bubble-mediated motion of the donor material. The cavitation bubble provides the transient mechanical bridge between optical energy deposition and the hydrodynamic ejection process. This chapter presents LIFT from a multiscale perspective centered on bubble dynamics and material ejection. It first reviews major LIFT donor architectures. Then, it examines how donor ribbon design, absorbing-layer properties, laser parameters, material rheology, control bubble inception/growth, jet formation, droplet breakup, and final deposition. Modeling approaches are discussed as tools for connecting experimental observations across time and length scales, ranging from reduced-order estimates to interface-resolving simulations and data-driven process maps. As one illustrative mechanistic example, thermal-only, plasma-mediated, and coupled plasma-thermal-thermoelastic frameworks for early-stage bubble inception are briefly compared to show how different inception assumptions can provide initial conditions for downstream bubble growth and jetting models. This chapter concludes by identifying opportunities for bubble-aware donor design, time-resolved diagnostics, benchmark datasets, and predictive LIFT process maps based on intermediate bubble and jet observables.

physics.flu-dyn↗

arXiv2026

From Optical Breakdown to Bubble Inception: A Coupled Plasma-Thermal Framework for Nanosecond Laser-Induced Cavitation in Water

Laser-induced cavitation under nanosecond optical breakdown is central to applications such as laser-induced forward transfer (LIFT), microsurgery, and microfluidic actuation, yet the physical origin of the earliest cavity and its connection to subsequent bubble growth remain unresolved. Existing models typically describe bubble formation either as a plasma-driven mechanical response or as a thermally driven nucleation process, without resolving how these mechanisms interact during inception. In this study, we developed a coupled plasma-thermal framework that unifies free-electron dynamics, plasma absorption, thermoelastic acoustic response, residual thermal energy retention, and post-inception bubble evolution within a single description. The model shows that bubble inception is governed primarily by plasma-induced thermoelastic acoustic relaxation, which generates transient tensile rarefaction pressures sufficient for cavitation on nanosecond timescales, while residual thermal energy sustains subsequent bubble growth. Because plasma-mediated energy deposition is spatially anisotropic under moving breakdown conditions, the initial cavity is predicted to form as an elongated, asymmetric bubble rather than as a spherical nucleus. This initial bubble shape reflects the spatial structure of the plasma absorption region and provides the starting geometry for subsequent bubble dynamics. Comparison with time-resolved experiments demonstrates that the coupled framework captures both early-time cavity formation and long-time bubble expansion more accurately than plasma-only or thermal-only models. These results show how breakdown-induced asymmetry controls the birth shape of the cavitation bubble and its early fluid-mechanical evolution, providing a physics-based basis for modeling laser-driven liquid motion and material transport.

physics.flu-dyn↗

arXiv2026

Tunable thermal conductivity through dual spin-phonon coupling in van der Waals ferromagnetic insulator Cr2Ge2Te6

The active manipulation of phonon transport remains a central challenge in phononics and spin caloritronics due to the charge-neutral nature of heat carriers. Spin-phonon coupling (SPC) offers a promising route for the dynamic control of heat carriers, yet its progress has been limited due to the lack of a unified framework and suitable material platforms. Here, we report on the magnetic field-tunable phonon transport behavior in the ferromagnetic insulator Cr2Ge2Te6. We observed two distinct anomalous regimes at both the high and low fields that were governed by isotropic magnon-phonon hybridization and an anisotropic magnon softening process, respectively. By integrating detailed transport behavior with Brillouin light scattering and ferromagnetic resonance, we uncovered the microscopic origins of these anomalous regimes and demonstrated that both the field magnitude and orientation could act as versatile tuning knobs to manipulate the thermal conductivity. Our findings provide experimental evidence of the SPC effect on phonon transport, demonstrating the dual impact of SPC within a unified system. This work will not only broaden the fundamental understanding of quasiparticle interactions but also establish a viable framework for dynamic phonon engineering. Furthermore, the characteristics of this system highlight the potential for achieving field-tunable phonon transport in similar platforms such as two-dimensional (2D) magnetic materials.

cond-mat.str-el↗

arXiv2025

A Geometric Pathway for Tuning Ferroelectric Properties via Polar State Reconfiguration

We report the discovery of a geometric pathway for tuning ferroelectric properties through thermally driven reconfiguration between coexisting polar states in Li-substituted NaNbO3. Using first-principles density functional theory calculation and 7Li solid-state nuclear magnetic resonance spectroscopy measurement, we reveal that Li substitution creates two distinct polar configurations whose transformation under annealing enhances the Curie temperature and induces piezoelectric hardening. Our findings establish a geometrically-driven polar state reconfiguration mechanism, providing a general design principle for ferroics whereby macroscopic functional properties can be engineered via lattice geometry.

cond-mat.mtrl-sci↗

arXiv2025

Oscillating Heat Transfer Prediction in Porous Structures Using Generative AI-Assisted Explainable Machine Learning

Predicting and interpreting thermal performance under oscillating flow in porous structures remains a critical challenge due to the complex coupling between fluid dynamics and geometric features. This study introduces a data-driven wGAN-LBM-Nested_CV framework that integrates generative deep learning, numerical simulation based on the lattice Boltzmann method (LBM), and interpretable machine learning to predict and explain the thermal behavior in such systems. A wide range of porous structures with diverse topologies were synthesized using a Wasserstein generative adversarial network with gradient penalty (wGAN-GP), significantly expanding the design space. High-fidelity thermal data were then generated through LBM simulations across various Reynolds (Re) and Strouhal numbers (St). Among several machine learning models evaluated via nested cross-validation and Bayesian optimization, XGBoost achieved the best predictive performance for the average Nusselt number (Nu) (R^2=0.9981). Model interpretation using SHAP identified the Reynolds number, Strouhal number, porosity, specific surface area, and pore size dispersion as the most influential predictors, while also revealing synergistic interactions among them. Threshold-based insights, including Re > 75 and porosity > 0.6256, provide practical guidance for enhancing convective heat transfer. This integrated approach delivers both quantitative predictive accuracy and physical interpretability, offering actionable guidelines for designing porous media with improved thermal performance under oscillatory flow conditions.

physics.flu-dyn↗

arXiv2025

First-principles phonon physics using the Pheasy code

Parameter-free calculations of lattice dynamics from first principles have achieved significant progress in the past decades, with a wealth of applications in thermodynamics, phase transitions, and transport properties of materials. Current approaches to derive the interatomic force constants (IFCs) of lattice potential become challenging and sometimes infeasible when going beyond third-order anharmonicity, due to the combinatorial explosion in the number of higher-order IFCs. In this work, we present a robust and user-friendly program, Pheasy, which accurately reconstructs the potential energy surface of crystalline solids via a Taylor expansion of arbitrarily high order. Given force-displacement datasets, the program enables an efficient and accurate extraction of IFCs using advanced machine-learning algorithms, and further calculates a wide range of harmonic and anharmonic phonon related properties. We show in three prototypical examples how the obtained IFCs have been successfully applied to study anharmonic lattice dynamics and thermal transport. Through these detailed benchmarks, we have also identified the optimal approach for IFC extractions and offered general guidelines for high-fidelity lattice-dynamical simulations, addressing the large uncertainties in the IFCs extracted from existing various schemes. Overall, the Pheasy project aims to create a phonon code ecosystem that connects diverse phonon simulation platforms and offers access to the broad research community.

cond-mat.mtrl-sci↗

arXiv2025

Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials

Magnetic machine-learning potentials (MLPs) now reach near-first-principles accuracy on the spin-lattice potential energy surface, but the dynamics and sampling frameworks that convert this accuracy into quantitative finite-temperature thermodynamics have lagged behind. Landau-Lifshitz-Gilbert spin-lattice dynamics fixes the local moment magnitude and incurs $O(N)$ MLP evaluations per integration step, while hybrid molecular-dynamics/Monte-Carlo lacks rigorous isothermal-isobaric sampling and remains expensive. We introduce TSPIN, which promotes the spin to a canonical pair $(\mathbf{S}_i,\boldsymbol{\pi}_i)$ alongside the lattice $(\mathbf{R}_i,\mathbf{p}_i)$ within a Nos\'e-Hoover-chain / Martyna-Tobias-Klein construction, delivering rigorous canonical and isothermal-isobaric sampling, native access to longitudinal spin fluctuations through an unconstrained spin amplitude, and one MLP evaluation per integration step. Applied to itinerant Co and localized multiferroic BiFeO$_3$, TSPIN matches the MD/MC reference thermodynamics of Co at substantially lower cost and reproduces the Curie and N\'eel temperatures within $\sim 7\%$ and $\sim 2\%$ of experiment, respectively. The same unconstrained-amplitude dynamics resolves contrasting spin-amplitude behavior: pronounced spin-modulus softening in Co, but a nearly temperature-independent high-spin Fe$^{3+}$ moment in BiFeO$_3$. TSPIN thereby promotes magnetic MLPs from accurate energy models to predictive finite-temperature simulation engines.

cond-mat.mtrl-sci↗

arXiv2025

Additive Manufacturing of Lunar Regolith for Reconfigurable Building Blocks toward Lunar Habitation

Utilizing locally available materials is a crucial step towards sustainable planetary habitation. Lunar regolith has gained tremendous interest in additive manufacturing in the past decades. However, due to the constrained manufacturing facilities and materials on the moon, many existing additive manufacturing methods are not suitable for practical on-site manufacturing. Here, we envision that light-based direct sintering of lunar regolith can be a feasible approach. Instead of directly manufacturing large structures, we hypothesize that small-scale, reconfigurable building blocks can be an alternative to form large and complex structures. To verify the feasibility, we conducted laser sintering of lunar regolith simulants as a proof of concept, following a simple theoretical calculation for direct sintering using the light available in space. Different laser processing parameters are investigated to obtain controllable lunar regolith sintering. We further designed Lego-like interlocking bricks that are reconfigurable for different structure assemblies without additional material. Mechanical performance (compressive strength) of sintered cubic blocks is evaluated, showing a peak stress of ~1.5 MPa. We hope this work will inspire other in-space manufacturing techniques and enable low-cost space habitation.

astro-ph.IM↗

arXiv2025

Machine learning-enabled atomistic insights into phase boundary engineering of solid-solution ferroelectrics

Atomistic control of phase boundaries is crucial for optimizing the functional properties of solid-solution ferroelectrics, yet their microstructural mechanisms remain elusive. Here, we harness machine-learning-driven molecular dynamics to resolve the phase boundary behavior in the KNbO3-KTaO3 (KNTO) system. Our simulations reveal that chemical composition and ordering enable precise modulation of polymorphic phase boundaries (PPBs), offering a versatile pathway for materials engineering. Diffused PPBs and polar nano regions, predicted by our model, highly match with experiments, underscoring the fidelity of the machine-learning atomistic simulation. Crucially, we identify elastic and electrostatic mismatches between ferroelectric KNbO3 and paraelectric KTaO3 as the driving forces behind complex microstructural evolution. This work not only resolves the longstanding microstructural debate but also establishes a generalizable framework for phase boundary engineering toward next-generation high-performance ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2025

DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research.

physics.chem-ph↗

arXiv2025

Integrating Deep-Learning-Based Magnetic Model and Non-Collinear Spin-Constrained Method: Methodology, Implementation and Application

We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic phenomena that requires large-scale simulations at the atomic level. First, we propose a basis-independent projection method for calculating atomic magnetic moments by applying a radial truncation to numerical atomic orbitals. A double-loop Lagrange multiplier method is utilized to ensure the satisfaction of constraint conditions while achieving accurate magnetic torque. The method is implemented in ABACUS with both plane wave basis and numerical atomic orbital basis. We benchmark the iron (Fe) systems and analyze differences from calculations with the plane wave basis and numerical atomic orbitals basis in describing magnetic energy barriers. Based on an automated workflow composed of first-principles calculations, magnetic model, active learning, and dynamics simulation, more than 30,000 first-principles data with the information of magnetic torque are generated to train a deep-learning-based magnetic model DeePSPIN for the Fe system. By utilizing the model in large-scale molecular dynamics simulations, we successfully predict Curie temperatures of alpha-Fe close to experimental values.

cond-mat.mtrl-sci↗

arXiv2025

ABACUS: An Electronic Structure Analysis Package for the AI Era

ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It mainly features density functional theory (DFT) and molecular dynamics functions and is compatible with both plane-wave basis sets and numerical atomic orbital basis sets. ABACUS serves as a platform that facilitates the integration of various electronic structure methods, such as Kohn-Sham DFT, stochastic DFT, orbital-free DFT, and real-time time-dependent DFT, etc. In addition, with the aid of high-performance computing, ABACUS is designed to perform efficiently and provide massive amounts of first-principles data for generating general-purpose machine learning potentials, such as DPA models. Furthermore, ABACUS serves as an electronic structure platform that interfaces with several AI-assisted algorithms and packages, such as DeePKS-kit, DeePMD, DP-GEN, DeepH, DeePTB, HamGNN, etc.

cond-mat.mtrl-sci↗

arXiv2024

Origin of Increased Curie Temperature in Lithium-Substituted Ferroelectric Niobate Perovskite: Enhancement of the Soft Polar Mode

The functionality of ferroelectrics is often constrained by their Curie temperature, above which depolarization occurs. Lithium (Li) is the only experimentally known substitute that can increase the Curie temperature in ferroelectric niobate-based perovskites, yet the mechanism remains unresolved. Here, the unique phenomenon in Li-substituted KNbO3 is investigated using first-principles density functional theory. Theoretical calculations show that Li substitution at the A-site of perovskite introduces compressive chemical pressure, reducing Nb-O hybridization and associated ferroelectric instability. However, the large off-center displacement of the Li cation compensates for this reduction and further enhances the soft polar mode, thereby raising the Curie temperature. In addition, the stability of the tetragonal phase over the orthorhombic phase is predicted upon Li substitution, which reasonably explains the experimental observation of a decreased orthorhombic-to-tetragonal phase transition temperature. Finally, a metastable anti-phase polar state in which the Li cation displaces oppositely to the Nb cation is revealed, which could also contribute to the variation of phase transition temperatures. These findings provide critical insights into the atomic-scale mechanisms governing Curie temperature enhancement in ferroelectrics and pave the way for designing advanced ferroelectric materials with improved thermal stability and functional performance.

cond-mat.mtrl-sci↗

arXiv2024

Phase transitions in typical fluorite-type ferroelectrics

While ferroelectric hafnia ($\mathrm{HfO_2}$) has become a technically important material for microelectronics, the physical origin of its ferroelectricity remains poorly understood. The tetragonal $P4_2/nmc$ phase is commonly assigned as its paraelectric mother phase but has no soft mode at the Brillouin zone center. In this work, we propose that the paraelectric-ferroelectric transition in hafnia-like $Pca2_1$ ferroelectric family can be described by a $Pcca$-$Pca2_1$ transition, where the $Pcca$ mother phase will evolve into either the $Pca2_1$ ferroelectric phase or the centrosymmetric $P2_1/c$ monoclinic phase, depending on the strain conditions. The $Pcca$ phase is directly linked to both phases in the context of continuous phase transition. Hafnia is regarded as a special case of this family, in that it has accidental atomic degeneracy because all anions are oxygen. The theory is also correlated to the seven-coordination theory that explains the ferroelectricity in hafnia from a chemical perspective. In addition, the strain conditions to promote the ferroelectric phase in hafnia are discussed.

cond-mat.mtrl-sci↗

arXiv2023

Van der Waals semiconductor InSe plastifies by phase transformation

Inorganic semiconductor materials are integral to various modern technologies, yet their brittleness and limited deformability/processability pose a significant challenge in the development of flexible, wearable, and miniaturized electronics. The recent discovery of room-temperature plasticity in a few inorganic semiconductors offers a promising pathway to address this challenge, but the deformation mechanisms of these materials remain unclear. Here, we investigate the deformation of InSe, a two-dimensional (2D) van der Waals (vdW) semiconductor with substantial plasticity. By developing a machine-learned deep potential, we perform atomistic simulations that capture the deformation features of hexagonal InSe upon out-of-plane compression. Surprisingly, we discover that InSe plastifies through a so-far unrecognized martensitic phase transformation; that is, the layered hexagonal structure is converted to a tetragonal lattice with specific orientation relationship. This observation is corroborated by high-resolution experimental observations and theory. It suggests a change of paradigm, where the design of new plastically-deformable inorganic semiconductors should focus on compositions and structures that favor phase transformations rather than traditional dislocation slip.

cond-mat.mtrl-sci↗

arXiv2023

A Spin-dependent Machine Learning Framework for Transition Metal Oxide Battery Cathode Materials

Owing to the trade-off between the accuracy and efficiency, machine-learning-potentials (MLPs) have been widely applied in the battery materials science, enabling atomic-level dynamics description for various critical processes. However, the challenge arises when dealing with complex transition metal (TM) oxide cathode materials, as multiple possibilities of d-orbital electrons localization often lead to convergence to different spin states (or equivalently local minimums with respect to the spin configurations) after ab initio self-consistent-field calculations, which causes a significant obstacle for training MLPs of cathode materials. In this work, we introduce a solution by incorporating an additional feature - atomic spins - into the descriptor, based on the pristine deep potential (DP) model, to address the above issue by distinguishing different spin states of TM ions. We demonstrate that our proposed scheme provides accurate descriptions for the potential energies of a variety of representative cathode materials, including the traditional Li$_x$TMO$_2$ (TM=Ni, Co, Mn, $x$=0.5 and 1.0), Li-Ni anti-sites in Li$_x$NiO$_2$ ($x$=0.5 and 1.0), cobalt-free high-nickel Li$_x$Ni$_{1.5}$Mn$_{0.5}$O$_4$ ($x$=1.5 and 0.5), and even a ternary cathode material Li$_x$Ni$_{1/3}$Co$_{1/3}$Mn$_{1/3}$O$_2$ ($x$=1.0 and 0.67). We highlight that our approach allows the utilization of all ab initio results as a training dataset, regardless of the system being in a spin ground state or not. Overall, our proposed approach paves the way for efficiently training MLPs for complex TM oxide cathode materials.

cond-mat.mtrl-sci↗

arXiv2023

Deep Learning Illuminates Spin and Lattice Interaction in Magnetic Materials

Atomistic simulations hold significant value in clarifying crucial phenomena such as phase transitions and energy transport in materials science. Their success stems from the presence of potential energy functions capable of accurately depicting the relationship between system energy and lattice changes. In magnetic materials, two atomic scale degrees of freedom come into play: the lattice and the spin. However, accurately tracing the simultaneous evolution of both lattice and spin in magnetic materials at an atomic scale is a substantial challenge. This is largely due to the complexity involved in depicting the interaction energy precisely, and its influence on lattice and spin-driving forces, such as atomic force and magnetic torque, which continues to be a daunting task in computational science. Addressing this deficit, we present DeepSPIN, a versatile approach that generates high-precision predictive models of energy, atomic forces, and magnetic torque in magnetic systems. This is achieved by integrating first-principles calculations of magnetic excited states with deep learning techniques via active learning. We thoroughly explore the methodology, accuracy, and scalability of our proposed model in this paper. Our technique adeptly connects first-principles computations and atomic-scale simulations of magnetic materials. This synergy presents opportunities to utilize these calculations in devising and tackling theoretical and practical obstacles concerning magnetic materials.

cond-mat.mtrl-sci↗